Link Function: information

The American College Catalog Study Database (CCS) contains academic data on 286 four-year colleges and universities in the United States. CCS is one of two databases produced by the Colleges and Universities 2000 project based at the University of California-Riverside. The CCS database comprises a sampled subset of institutions from the related Institutional Data Archive (IDA) on American Higher Education (ICPSR 34874). Coding for CCS was based on college catalogs obtained from College Source, Inc. The data are organized in a panel design, with measurements taken at five-year intervals: academic years 1975-76, 1980-81, 1985-86, 1990-91, 1995-96, 2000-01, 2005-06, and 2010-11. The database is based on information reported in each institution's college catalog, and includes data regarding changes in major academic units (schools and colleges), departments, interdisciplinary programs, and general education requirements. For schools and departments, changes in structure were coded, including new units, name changes, splits in units, units moved to new schools, reconstituted units, consolidated units, departments reduced to program status, and eliminated units.

The Frozen Four is a women's ice hockey championship which is the culmination of the college hockey season. The women's hockey team from Wisconsin, nicknamed the Badgers, won a record 35 Frozen Four games.

This study was created, by the National Collegiate Athletic Association (NCAA), to provide public access to team-level Academic Progress Rates (APRs), eligibility rates, retention rates, and penalty and award information on Division I student-athletes starting with the 2003-2004 season through the 2013-2014 season, as well as to provide efficient analysis and linking of these data to other educational data.

Currently, college athletes in the United States are not paid for their performance. During a survey in the United States in March 2023, around one third of respondents stated that they believed student athletes should be paid for their sport. Meanwhile, just under half percent of respondents disagreed, and 19 percent were unsure or had no opinion.

Download the data that appears on the College Scorecard, as well as supporting data on student completion, debt and repayment, earnings, and more. Last updated on 4-19-2023.

Data product is provided by ASL Marketing. It contains current college students who are attending colleges and universities nationwide. Connect with this market by: Class Year Field of Study Home/School address College Attending Ethnicity School Type Region Sports Conference Gender eSports Email

Analysis of ‘College Basketball Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/andrewsundberg/college-basketball-dataset on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Content

Data from the 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, and 2021 Division I college basketball seasons.

cbb.csv has seasons 2013-2019 combined

The 2020 season's data set is kept separate from the other seasons, because there was no postseason due to the Coronavirus.

The 2021 data is from 3/15/2021 and will be updated and added to cbb.csv after the tournament

Variables

RK (Only in cbb20): The ranking of the team at the end of the regular season according to barttorvik

TEAM: The Division I college basketball school

CONF: The Athletic Conference in which the school participates in (A10 = Atlantic 10, ACC = Atlantic Coast Conference, AE = America East, Amer = American, ASun = ASUN, B10 = Big Ten, B12 = Big 12, BE = Big East, BSky = Big Sky, BSth = Big South, BW = Big West, CAA = Colonial Athletic Association, CUSA = Conference USA, Horz = Horizon League, Ivy = Ivy League, MAAC = Metro Atlantic Athletic Conference, MAC = Mid-American Conference, MEAC = Mid-Eastern Athletic Conference, MVC = Missouri Valley Conference, MWC = Mountain West, NEC = Northeast Conference, OVC = Ohio Valley Conference, P12 = Pac-12, Pat = Patriot League, SB = Sun Belt, SC = Southern Conference, SEC = South Eastern Conference, Slnd = Southland Conference, Sum = Summit League, SWAC = Southwestern Athletic Conference, WAC = Western Athletic Conference, WCC = West Coast Conference)

G: Number of games played

W: Number of games won

ADJOE: Adjusted Offensive Efficiency (An estimate of the offensive efficiency (points scored per 100 possessions) a team would have against the average Division I defense)

ADJDE: Adjusted Defensive Efficiency (An estimate of the defensive efficiency (points allowed per 100 possessions) a team would have against the average Division I offense)

BARTHAG: Power Rating (Chance of beating an average Division I team)

EFG_O: Effective Field Goal Percentage Shot

EFG_D: Effective Field Goal Percentage Allowed

TOR: Turnover Percentage Allowed (Turnover Rate)

TORD: Turnover Percentage Committed (Steal Rate)

ORB: Offensive Rebound Rate

DRB: Offensive Rebound Rate Allowed

FTR : Free Throw Rate (How often the given team shoots Free Throws)

FTRD: Free Throw Rate Allowed

2P_O: Two-Point Shooting Percentage

2P_D: Two-Point Shooting Percentage Allowed

3P_O: Three-Point Shooting Percentage

3P_D: Three-Point Shooting Percentage Allowed

ADJ_T: Adjusted Tempo (An estimate of the tempo (possessions per 40 minutes) a team would have against the team that wants to play at an average Division I tempo)

WAB: Wins Above Bubble (The bubble refers to the cut off between making the NCAA March Madness Tournament and not making it)

POSTSEASON: Round where the given team was eliminated or where their season ended (R68 = First Four, R64 = Round of 64, R32 = Round of 32, S16 = Sweet Sixteen, E8 = Elite Eight, F4 = Final Four, 2ND = Runner-up, Champion = Winner of the NCAA March Madness Tournament for that given year)

SEED: Seed in the NCAA March Madness Tournament

YEAR: Season

Acknowledgements

This data was scraped from from http://barttorvik.com/trank.php#. I cleaned the data set and added the POSTSEASON, SEED, and YEAR columns

--- Original source retains full ownership of the source dataset ---

This dataset contains locations and attributes of University and College, created as part of the DC Geographic Information System (DC GIS) for the Office of the Chief Technology Officer (OCTO) and participating D.C. government agencies. Information provided by OCTO, EMA, and other sources identified as University Areas and DC GIS staff geo-processed the data. This layer does not represent university areas contained in the campus plans from the DC Office of Zoning.

Data from the Ministry of Colleges and Universities' College Enrolment Statistical Reporting system.

Provides aggregated key enrolment data for college students, such as:

• Fall term headcount enrolment by campus, credential pursued and level of study
• Fall term headcount enrolment by program and Classification of Instructional Program
• Fall term headcount enrolment by student status in Canada and country of citizenship by institution
• Fall term headcount enrolment by student demographics (e.g., gender, age, first language)

To protect privacy, numbers are suppressed in categories with less than 10 students.

Related

• College enrolments - 1996 to 2011
• University enrolment
• Enrolment by grade in secondary schools
• School enrolment by gender
• Second language course enrolment
• Course enrolment in secondary schools
• Enrolment by grade in elementary schools

College sports in North America is a big business and The National Collegiate Athletic Association is responsible for regulating the student athletes. Approximately 171.24 million U.S. dollars of NCAA revenue was allocated towards the basketball fund in 2022/23. NCAA revenue Within the National Collegiate Athletic Association (NCAA), universities are organized into three divisions, D1, D2 and D3. The largest universities with a minimum of 14 sports for males and females are classified as Division I schools. These universities heavily invest in sports facilities, offer more scholarships, and have large budgets. Division II schools also offer scholarships for athletes, but are usually smaller than Division I schools. Between television and marketing rights fees, championships & NIT tournaments, sales and services, which are the most profitable segments for NCAA, the association generated over one billion U.S. dollars in annual revenue. Division I is the money maker As a non-profit organization, about 96 percent of NCAA’s total revenue is distributed to conference members and institutions, used for programs that benefit student-athletes or used to support championships. NCAA Division I, the association’s most important division, accounts for the largest share of NCAA expenses. In 2024, over 880 million U.S. dollars was spent on Division I members, and Division I championships, programs and NIT tournaments, while around 60 million U.S. dollars was dedicated to Division II matters.

Montgomery College Student Enrollment Data Update Frequency: Annually

We present evidence from a series of field experiments in college coaching/mentoring. We find large impacts on college attendance and persistence, but only in the treatments where we use an intensive boots-on-the-ground approach to helping students. Our treatments that provide financial incentives or information alone do not appear to be effective. For women, assignment to our mentoring treatment yields a 15 percentage point increase in the college-going rate while treatment on the treated estimates are 30 percentage points (against a control complier mean rate of 43 percent). We find much smaller treatment effects for men, and the difference in treatment effects across genders is partially explained by the differential in self-reported labor market opportunities. We do not find evidence that the treatment effect derives from simple behavioral mistakes, student disorganization, or a lack of easily obtained information. Instead our mentoring program appears to substitute for the potentially expensive and often missing ingredient of skilled parental or teacher time and encouragement.

Alesco's college and alumni data contains demographic information on almost every college student in the nation. Nowhere else will you find more complete and accurate information on student and alumni, individuals by name and age and career interests along with detailed financial-related data including income.

Our student data is built by utilizing hundreds of sources including public records, directories, county recorder and tax assessor files, US Census data, surveys, and purchase transactions. We continuously utilize USPS processing routines to give you the most complete and up-to-date addresses.

Flexible pricing available to meet all your business needs. Student Data is available on a transactional basis or unlimited use cases for marketing and analytics.

This sample covers forecast grade TV viewership data. PredictHQ’s Live TV Events data includes the seven top US leagues: NFL, NBA, NHL, MLB, D1 NCAA Basketball, D1 NCAA Football, and MLS. The data also includes the top 100 sports games based on viewership that includes golf tournaments and boxing matches by county level. Filter by city or county to access relevant data rather than by broad designated market areas (DMAs) used by other providers.

Customers can use televised sports viewership data to anticipate where demand for their products will be highest, and plan accordingly. Insight into high-viewership sports games combined with an understanding of how these events historically impact them unlocks the ability for these businesses to: 1) Better align inventory levels with local demand levels to process the influx of orders 2) Staff enough drivers to meet increased demand for deliveries and 3) Better mobilize drivers to ensure fast delivery times. For example, pizza deliveries may skyrocket during football games and having insight into the counties or locations that will be impacted by this demand most depending on the teams playing is key.

Location: California Visibility Window: 1 month period (July 2023) Categories: Live TV Events Viewership Features Data

Fields Included: - phq_viewership_sports_american_football_stats_count - phq_viewership_sports_american_football_stats_sum - phq_viewership_sports_american_football_stats_min - phq_viewership_sports_american_football_stats_avg - phq_viewership_sports_baseball_stats_count - phq_viewership_sports_baseball_stats_sum - phq_viewership_sports_baseball_stats_min - phq_viewership_sports_baseball_stats_max - phq_viewership_sports_baseball_stats_avg - phq_viewership_sports_basketball_stats_count - phq_viewership_sports_basketball_stats_sum - phq_viewership_sports_basketball_stats_min - phq_viewership_sports_basketball_stats_max - phq_viewership_sports_basketball_stats_avg - phq_viewership_sports_ice_hockey_stats_count - phq_viewership_sports_ice_hockey_stats_sum - phq_viewership_sports_ice_hockey_stats_min - phq_viewership_sports_ice_hockey_stats_max - phq_viewership_sports_ice_hockey_stats_avg - phq_viewership_sports_soccer_stats_count - phq_viewership_sports_soccer_stats_sum - phq_viewership_sports_soccer_stats_min - phq_viewership_sports_soccer_stats_max - phq_viewership_sports_soccer_stats_avg

Data quality: PredictHQ's data quality is one of its key strengths: 1) We have developed a set of Quality Standards for Processing Demand Causal Factors (QSPD), which are used to define the criteria for high-quality event data. By following these standards, PredictHQ ensures that their data meets the highest levels of quality. 2) We use more than 450 data sources to collect event data, including public records, social media, and ticketing websites. 3) We have built thousands of machine learning models that standardize, verify, enrich, and rank every single event. 4) On average we process 28 million events and 422,000 entities every day 5) We track the quality of our data over time and make improvements as needed.

About PredictHQ: PredictHQ is the world’s first and only company that provides the missing context for the biggest external factor that impacts businesses demand – events. PredictHQ’s intelligent data of verified global events enables businesses to forecast shifts in demand from events to be able to adjust their inventory, make changes to labor, dynamically price and operate more efficiently. Think conferences, sports games, college graduations, floods, and more. PredictHQ brings all events into one place, combines it with world-first tools and intelligence to allow organizations to better predict and respond to changing customer demand created by events in an easy, reliable, and scalable way. We meet customers exactly where they are, ensuring they can access our data the way that suits them best.

Learn more about PredictHQ's real-world event data by visiting our Developer and Data Science Documentation: https://docs.predicthq.com/ Or from our website: https://www.predicthq.com/features/live-tv-events

Keywords: attended events, attendance, sports, festivals, expos, conferences, concerts, performing arts, community, polygon, consumer spending, predicted spend, location information, demand intelligence, financial data, venue location, accommodation, transportation, restaurant, demand intelligence, event intelligence, event categorisation, business insights, event tracking, historical event data, even impact analysis, event-driven decisions, predictive analytics,

Three psychological skill profiles were found among athletes in each of the three NCAA Divisions. They revealed significant differences in the amounts of psychological practices in Divisions I and II and precompetition anxiety in all three divisions. Findings suggest that psychological practice is related to more adaptive psychological skill profiles.

Vietnam University & College: Student data was reported at 2,118.500 Person th in 2015. This records a decrease from the previous number of 2,363.900 Person th for 2014. Vietnam University & College: Student data is updated yearly, averaging 1,131.022 Person th from Sep 1991 (Median) to 2015, with 25 observations. The data reached an all-time high of 2,363.900 Person th in 2014 and a record low of 106.900 Person th in 1991. Vietnam University & College: Student data remains active status in CEIC and is reported by General Statistics Office. The data is categorized under Global Database’s Vietnam – Table VN.G050: Education Statistics.

A community college district is a geographical area that serves to operate local community colleges within its boundaries.

The data and STATA code files included are part of our team's study of the marginal revenue product of elite college football players. Included are: a CSV file containing all of the original data, a .do file with the regressions/models included in this paper, and a .dta file containing recruiting data used later in the paper. Data covers the 2006-2015 seasons.

.COLLEGE Whois Database, discover comprehensive ownership details, registration dates, and more for .COLLEGE TLD with Whois Data Center.